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> I appealed and they asked me to send a photo. I sent them a photo and they said I was still a robot and there was no further appeal. That's the plot of Blade
by _pmf_ 8y ago
> I appealed and they asked me to send a photo. I sent them a photo and they said I was still a robot and there was no further appeal.
That's the plot of Blade Runner 2.
- mcv 8y agoI would have sent them a photo of a robot.
- lloeki 8y agoI would have disguised as a robot and sent them a picture of me. Ironically sending a robot picture is the best proof that you're not one, since robots don't have a sense of humour (yet). Wait; lightbulb moment; someone has to run a NN against a dad joke DB and learn AI to crack some (like the generated face stuff). Well this is the internets and all, so I suppose it's been done before but no one would admit to owning such a big enough dad joke DB...
- tripzilch 8y ago> someone has to run a NN against a dad joke DB and learn AI to crack some A "that's what she said" detector can be implemented as a two class identification problem. This 2011 describes one algorithm that achieves 71% precision: That's What She Said: Double Entendre Identification https://www.aclweb.org/anthology/P11-2016 https://www.aclweb.org/anthology/P11-2016 I believe they could have done an even better job with better training (and test) data. It seems to me there's a big problem with how they selected the negative examples: one of the sources is a set of racy text messages, which would be rife with positives. They claim TWSS positives are rare in general, but I doubt that holds for a sexy text messages corpus. Their overall strategy is pretty interesting though (not deep learning).